import cnnbest_model
import torch
from load_ascad import load_ascad
import torch.nn.functional as F
import math

model_file_path = "./trained_model/cnn-last.pth"
data_file_path = "ASCAD.h5"

model_name = "cnn"

key_score = dict()
""" 密钥—分数字典 """

mval_key = dict()
""" 中间值-密钥字典 """


def cal_middleval(p_text, key):
    """ 计算sbox(p[3] xor k[3])
    注意要传两个数字型
    """
    s_box = [[0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76],
             [0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0],
             [0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F, 0xF7, 0xCC, 0x34, 0xA5, 0xE5, 0xF1, 0x71, 0xD8, 0x31, 0x15],
             [0x04, 0xC7, 0x23, 0xC3, 0x18, 0x96, 0x05, 0x9A, 0x07, 0x12, 0x80, 0xE2, 0xEB, 0x27, 0xB2, 0x75],
             [0x09, 0x83, 0x2C, 0x1A, 0x1B, 0x6E, 0x5A, 0xA0, 0x52, 0x3B, 0xD6, 0xB3, 0x29, 0xE3, 0x2F, 0x84],
             [0x53, 0xD1, 0x00, 0xED, 0x20, 0xFC, 0xB1, 0x5B, 0x6A, 0xCB, 0xBE, 0x39, 0x4A, 0x4C, 0x58, 0xCF],
             [0xD0, 0xEF, 0xAA, 0xFB, 0x43, 0x4D, 0x33, 0x85, 0x45, 0xF9, 0x02, 0x7F, 0x50, 0x3C, 0x9F, 0xA8],
             [0x51, 0xA3, 0x40, 0x8F, 0x92, 0x9D, 0x38, 0xF5, 0xBC, 0xB6, 0xDA, 0x21, 0x10, 0xFF, 0xF3, 0xD2],
             [0xCD, 0x0C, 0x13, 0xEC, 0x5F, 0x97, 0x44, 0x17, 0xC4, 0xA7, 0x7E, 0x3D, 0x64, 0x5D, 0x19, 0x73],
             [0x60, 0x81, 0x4F, 0xDC, 0x22, 0x2A, 0x90, 0x88, 0x46, 0xEE, 0xB8, 0x14, 0xDE, 0x5E, 0x0B, 0xDB],
             [0xE0, 0x32, 0x3A, 0x0A, 0x49, 0x06, 0x24, 0x5C, 0xC2, 0xD3, 0xAC, 0x62, 0x91, 0x95, 0xE4, 0x79],
             [0xE7, 0xC8, 0x37, 0x6D, 0x8D, 0xD5, 0x4E, 0xA9, 0x6C, 0x56, 0xF4, 0xEA, 0x65, 0x7A, 0xAE, 0x08],
             [0xBA, 0x78, 0x25, 0x2E, 0x1C, 0xA6, 0xB4, 0xC6, 0xE8, 0xDD, 0x74, 0x1F, 0x4B, 0xBD, 0x8B, 0x8A],
             [0x70, 0x3E, 0xB5, 0x66, 0x48, 0x03, 0xF6, 0x0E, 0x61, 0x35, 0x57, 0xB9, 0x86, 0xC1, 0x1D, 0x9E],
             [0xE1, 0xF8, 0x98, 0x11, 0x69, 0xD9, 0x8E, 0x94, 0x9B, 0x1E, 0x87, 0xE9, 0xCE, 0x55, 0x28, 0xDF],
             [0x8C, 0xA1, 0x89, 0x0D, 0xBF, 0xE6, 0x42, 0x68, 0x41, 0x99, 0x2D, 0x0F, 0xB0, 0x54, 0xBB, 0x16]]

    m_val = p_text ^ key
    row = m_val >> 4
    col = m_val & 0x0F
    return int(s_box[row][col])


if __name__ == "__main__":
    """ 初始化密钥-分数字典 """
    for i in range(256):
        key_score[i] = 0.0
    test_data_loader, plain_texts = load_ascad(data_file_path, 1, False, 1)

    # 不同模型类型测试 #
    if (model_name == "cnn"):
        cnn_best = cnnbest_model.CnnBest()
    else:
        print("错误，没有这个模型")

    if (torch.cuda.is_available()):
        cnn_best = cnn_best.cuda()

    # cpu使用以下语句
    # cnn_best = torch.load(model_file_path, map_location=torch.device('cpu'))
    cnn_best.load_state_dict(torch.load(model_file_path))

    # 计算准确度 #
    right_count = 0.0
    data_count = 0
    best_acc = 0.0
    with torch.no_grad():
        for i, data in enumerate(test_data_loader):
            att_trace, att_label = data
            if (torch.cuda.is_available()):
                att_trace = att_trace.cuda()
                att_label = att_label.cuda()
            outputs = cnn_best(att_trace)
            # 中间值分数 #
            softmax_outputs = F.softmax(outputs, dim=1)

            # 生成中间值-密钥字典 #
            for k in range(256):
                mval_key[cal_middleval(plain_texts[i].item(), k)] = k

            # 将中间值分数赋给密钥 #
            for mv in range(256):
                a = key_score[mval_key[mv]]
                b = softmax_outputs[0][mv].item()
                if (b != 0):
                    key_score[mval_key[mv]] = a + math.log(b, 2)
                else:
                    key_score[mval_key[mv]] = a - 9999999

            # 对字典进行排序 #
            key_score_ = sorted(key_score.items(), key=lambda d: d[1], reverse=True)
            print(key_score_)
            print(f"第{i + 1}条能量迹")
            input()

            predicted = torch.argmax(outputs, dim=1).item()

            # # Test #
            # if (i == 2):
            #     print(plain_texts[i])
            #     print(cal_middleval(plain_texts[i].item(), 224))
            #     print(att_label[0].item())
            #     print(predicted)
            #     exit()

            if (predicted == att_label.item()):
                right_count += 1
            print('\r', f"进度：{i + 1}/{len(test_data_loader)}", end="")
            data_count += 1
    accuracy = right_count / data_count
    print(f" 准确度: {accuracy}")
